The number of sensors is increasing. Sensors commonly used include sensors for temperature, humidity, oxygen level, pH, depth, wind, rain, speed, acceleration, movement and so forth. Commonly a sensed value, of an observation, is used as a basis for some kind of action, or saved for later purposes. Hereinafter the term “observation” is used for a registered measurement sensed by a sensor without limitation to the described examples. The sensors may be used in a large spectrum of appliances: indoor, outdoor, various industrial appliances, vehicle mounted, to mention a few examples.
The environments monitored by sensors in this way may include any indoor or outdoor locations of any size, such as rooms requiring controlled conditions and more open spaces and locations of interest. Sensors may also monitor “objects” such as machines and humans, or the environment around or inside the objects. A few examples, out of a vast number of examples of the monitoring of objects are monitoring of the functioning of a human heart via a pacemaker, the environmental conditions in a vehicle cab or vehicle engine and the battery status of a battery in a mobile phone or vehicle.
Sensor data may also be used for controlling a process, typically an automated process where sensor data is via a system influencing an actuator, depending on the sensor observation. Observations are further collected for future need or for future potential need. Collected observations may be used for analysis, prognosis, diagnosis, statistics or research. This drives a desire to collect observations.
A sensor itself typically has limited resources for computing and data storage. With today's kind of sensors it is not feasible that each sensor should store any significant amount of observations. For a sensor located at a point of interest, it is neither desired to have a large amount of requests from data users, to arrive at a single sensor. In the following, “data user” is used as a representative term for users of observations/sensor data. A data user may be an application or a device. A data user may also be e.g. a system for creation of weather prognosis, or a system in a factory that controls a production process. These are just examples, but the description is not limited to these examples. Therefore multiple sensors typically may be connected into networks, referred to as sensor networks. Via a sensor network observations are often collected by a centralized computing and storage facility, typically a data server. This is simply to avoid the need for sensors to store all observations themselves, potentially receive large amounts of requests from data users, and to more easily utilize the collected observations. Such sensor networks may therefore become quite large and complex, e.g. including several different types of sensors installed at different locations, as well as communication links, gateways and servers for collecting and conveying observation results from the sensors to appropriate receiving parties.
Today there is an exponential growth of the usage of such sensor networks. With the increase of sensor networks and increase of observations availability for users, the requirement on data management and processing capability increases. It is already today a growth of observations data, data collected from sensors, and with the increased number of network-connected sensors, there may also be an exponential growth of data in the future. There are today solutions for limiting observations stored in databases. These solutions propose to detect duplicate data, and remove the duplicates, or over time remove old and expired data.
A problem with the above mentioned proposals to restrict the quantity of data or restrict the growth of data, is that there is a risk of either to keep too much data, or to remove too much data. The problem with today's solutions is how to remove unnecessary data without wasting useful data. Removal of data is necessary to avoid databases which otherwise virtually have to grow to an infinite size. It also necessary to keep data within a manageable size in order to meet data users' requirements on observations availability. Too large quantities of data will prevent data users to find a desired observation, or will require unnecessary resources to find relevant observations. However removing too much of the data, might decrease the usefulness of the sensors for data users. A question is to decide which sensor data to maintain and which data to remove from a database. A problem related to this question is how to technically implement a way of decreasing and/or automate the handling of sensor data in a computerized system.